Neural Random Forests

  title={Neural Random Forests},
  author={G{\'e}rard Biau and Erwan Scornet and Johannes Welbl},
  journal={Sankhya A},
  pages={347 - 386}
Given an ensemble of randomized regression trees, it is possible to restructure them as a collection of multilayered neural networks with particular connection weights. Following this principle, we reformulate the random forest method of Breiman (2001) into a neural network setting, and in turn propose two new hybrid procedures that we call neural random forests. Both predictors exploit prior knowledge of regression trees for their architecture, have less parameters to tune than standard… 

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